Target tracking systems, such as electro-optical and/or infrared (IR) systems, cover a wide range of distinct technologies based on the targets and the desired characteristic to be tracked. For example, tracking of celestial objects may involve the use of light in the visible spectrum and/or IR bands. Other types of optical detection may depend on heat (e.g., from exhaust), which produces significant radiation in the infrared (IR) spectral region. A spectral band choice may also be affected by changes and variations of atmospheric transmission and scattering. In the area of Unmanned Aerial Vehicles (UAVs), optical sensing and tracking for sense-and-avoid systems have become very important areas of research and development.
Target tracking techniques may generally be divided into point target tracking and fully imaged target tracking. For point targets, one of the challenges in target tracking is to extract or detect a target from a complex and cluttered background. For fully imaged targets (e.g., a vehicle on a road) the contrast between the target and background may be an important parameter. The detection of targets with low signal-to-noise ratio (SNR) is a key research challenge for modern computer vision systems. Track-before-detect (TBD) algorithms approach the problem by delaying detection decisions until after motion is captured, wherein the tracking occurs across multiple video frames—a feature critical for targets where any individual frame may contain insufficient information to distinguish signal from noise. Current state-of-the-art track-before-detect algorithms include particle filtering, which allows the target state distribution to be maintained and updated through Monte Carlo methods across frames, and velocity matched filters, which search over a space of velocity hypotheses and match an expected target profile against the image frames. Both techniques are capable of detecting targets with very low SNR in ideal conditions. However, both are computationally burdensome and too complex to perform well in a real-time processing environment.